A new inverse DEA model with frontier changes for analyzing the achievement path of CO2 emissions target of China in 2030

Abstract

China has promised to reduce its CO2 emissions per gross domestic product (GDP) by 60–65% relative to 2005 emissions by 2030, which puts higher requirements for the quality of China's future economic development. Meanwhile, using current methods to scientifically analyze the realization path of CO2 emission reduction while ensuring future economic development remains a challenge. This study develops a new inverse data envelopment analysis (DEA) method to analyze the annual realization path of CO2 emission reduction and economic growth targets in China from 2020 to 2030. This method not only considers undesirable output and frontier changes but also analyzes the realization path of CO2 emission reduction on the premise of ensuring economic growth. Moreover, the proposed method can analyze resource optimization allocation to achieve the corresponding goals, and its contributions to sustainable development are discussed. The results indicate that (1) In terms of CO2 emission reduction, the eastern region will face the largest pressure of CO2 emission reduction, accounting for 52.85% of the total CO2 emission reduction, followed by the central region, accounting for 37.2%, and the western region will face the least pressure, accounting for 9.95%; whereas in terms of the change trend of CO2 emission reduction, the eastern and central regions show opposite CO2 emission reduction trends, while the trend in the western region is relatively stable. (2) At provincial level, CO2 emission reduction shows a polarized distribution. Many provinces, such as Jiangsu, Guangdong, Hunan, and Chongqing, undertake great pressure to reduce CO2 emission. However, some provinces, such as Shandong, Shanxi, and Yunnan, almost have no potential to reduce CO2 emission while maintaining economic growth. (3) The increasement of human and energy resources input in the future is key to achieving CO2 emission reduction and economic development goals. Finally, some useful implications are summarized by analyzing the results to provide powerful decision support for achieving CO2 emission reduction and economic growth targets of China in 2030.